UNICEF Global Adolescent Mortality Story

Author

Prabhjas Singh (16815)

๐Ÿ“ Executive Summary & Project Details

A Comprehensive Visualization and Analysis Project

Name: Prabhjas Singh (16815)
University: Dublin City University
Program: MSc in Management (Strategy)
Module: BAA1030 โ€“ Data Analytics and Storytelling
Submission Date: 27 April 2025


Adolescents represent a pivotal stage of human development. Despite health advances, adolescent mortality remains a global concern.
This report explores adolescent mortality patterns (ages 10โ€“14 years), evaluating gender disparities, regional inequalities, temporal trends, and links with economic development.
Visualizations and insights aim to highlight global health gaps and support targeted intervention strategies.

Show code 
# Dataset loading
import pandas as pd
from plotnine import *
import plotly.express as px

indicator = pd.read_csv('unicef_indicator_1_cleaned.csv')
metadata = pd.read_csv('unicef_metadata_cleaned.csv')

indicator.columns = indicator.columns.str.strip().str.lower()
metadata.columns = metadata.columns.str.strip().str.lower()

data = pd.merge(indicator, metadata, on='country', how='left')
latest_year = data['time_period'].max()

๐ŸŒ Global Mortality Mapping: Regional Inequalities

Study Focus:
Visualize global mortality rates geographically.

Evaluation Goal:
Spot regional mortality hotspots where adolescents face extreme risks.

Show code 
data['country'] = data['country'].str.title()
latest_global_data = data[data['time_period'] == latest_year]

fig = px.choropleth(
    latest_global_data,
    locations="country",
    locationmode="country names",
    color="obs_value",
    color_continuous_scale=[
        (0.0, "rgb(255,245,245)"),
        (0.3, "rgb(255,200,200)"),
        (0.6, "rgb(255,100,100)"),
        (1.0, "rgb(178,34,34)")
    ],
    title="Global Distribution of Adolescent Mortality Rates",
    labels={'obs_value': 'Mortality Rate (per 1,000)'}
)

fig.show()

Insight:
Sub-Saharan Africa and South Asia remain mortality epicenters.

๐ŸŒ Global Gender Differences in Adolescent Mortality (Top 10 Countries)

Study Focus:
Analyzing gender gaps globally by highlighting the top 10 countries.

Evaluation Goal:
Identify where males face greater mortality challenges.

Show code 
latest_data = data[data['time_period'] == latest_year]
latest_data = latest_data[latest_data['sex'].isin(['Male', 'Female'])]
top10_countries = latest_data.groupby('country')['obs_value'].mean().sort_values(ascending=False).head(10).index.tolist()
top10_gender_data = latest_data[latest_data['country'].isin(top10_countries)]

(
    ggplot(top10_gender_data) +
    aes(x='country', y='obs_value', fill='sex') +
    geom_bar(stat='identity', position='dodge') +
    scale_fill_manual(values={"Male": "#17a2b8", "Female": "#e377c2"}) +
    theme_minimal() +
    labs(
        title='Top 10 Countries: Adolescent Mortality by Gender',
        x='Country',
        y='Mortality Rate (per 1,000)',
        fill='Gender'
    )
)

Insight:
Males face higher mortality risks globally, dominated by Sub-Saharan Africa.

๐Ÿ‡ณ๐Ÿ‡ฌ Nigeria Adolescent Mortality Trends: A National Case Study

Why Nigeria?
Nigeria has historically struggled with high adolescent mortality.

Show code 
nigeria = data[(data['country'] == 'Nigeria') & (data['time_period'] <= latest_year)]

(
    ggplot(nigeria) +
    aes(x='time_period', y='obs_value') +
    geom_line(color="#17a2b8", size=1.8) +
    geom_point(color='black', size=3) +
    theme_minimal() +
    labs(
        title='Nigeria Adolescent Mortality Rate Over Time',
        x='Year',
        y='Mortality Rate (per 1,000)'
    )
)

Insight:
Nigeria shows slow but steady improvement, but progress remains below global averages.

๐Ÿ’ต Economic Influence on Adolescent Survival

Study Focus:
Analyze the link between GDP and adolescent mortality.

Show code 
bins = [0, 1000, 5000, 10000, 50000, 1000000]
labels = ['<1k', '1k-5k', '5k-10k', '10k-50k', '>50k']
data['gdp_band'] = pd.cut(data['gdp per capita (constant 2015 us$)'], bins=bins, labels=labels)

latest_gender_data = data[(data['time_period'] == latest_year) & (data['sex'].isin(['Male', 'Female']))]
avg_band_mortality = latest_gender_data.groupby(['gdp_band', 'sex'])['obs_value'].mean().reset_index()

(
    ggplot(avg_band_mortality) +
    aes(x='gdp_band', y='obs_value', group='sex', color='sex') +
    geom_line(size=2) +
    geom_point(size=3) +
    scale_color_manual(values={"Male": "#17a2b8", "Female": "#e377c2"}) +
    theme_minimal() +
    labs(
        title='Average Mortality Rate by GDP Band (Gender Split)',
        x='GDP Band',
        y='Average Mortality Rate (per 1,000)',
        color='Gender'
    )
)

Insight:
Mortality drops sharply after $10,000 GDP per capita.

๐Ÿงน Conclusion: Addressing Global Adolescent Health Disparities

โœ… Key Findings: - Males are at higher mortality risk. - Regional and economic disparities persist. - Economic growth helps improve adolescent survival.

โœ… Recommendations: - Strengthen health infrastructure. - Promote gender-sensitive health policies. - Leverage economic development for youth health equity.

๐Ÿ“™ References